The WebCamT dataset is a collection of traffic scenes recorded using city-cameras introduced by [1]}. It is particularly challenging to analyze due to the low-resolution \((352\times 240)\) , high occlusion, and large perspective. We considered a total of about 40,000 images belonging to 10 different cameras and consequently having different views. We employed the existing bounding box annotations of the dataset to generate ground truth density maps. In particular, we considered one Gaussian Normal kernel for each vehicle present in the scene, having a value of \(\mu \)  and \(\sigma \)  equal to the center and proportional to the size of the bounding box surrounding the vehicle, respectively. We used this dataset to test performance with the Camera2Camera domain shift.
